The full text of this article

 

User behaviour modelling by abstracting low-level window transition logs
by Ryohei Saito; Tetsuji Kuboyama; Hiroshi Yasuda
International Journal of Computational Science and Engineering (IJCSE), Vol. 11, No. 3, 2015

 

Abstract: This paper proposes a novel framework for modelling user behaviour from low-level computer usage logs aiming to find working patterns and behaviours of employees at work. The logs we analyse are recorded in individual computers for employees in a company, and include active window transitions on display. Our framework consists of three levels of abstraction: 1) modelling user behaviour patterns by hidden Markov models; 2) clustering user behaviour models by kernel principal component analysis with a graph kernel; 3) extracting common patterns from clusters. The experimental results show that our method reveals implicit user behaviour at a high level of abstraction, and allows us to understand individual user behaviour among groups, and over time.

Online publication date: Fri, 23-Oct-2015

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com